The Minnesota Department of Transportation (MnDOT) has added MnPASS High Occupancy Toll (HOT) lanes on two freeway corridors in the Twin Cities. While not the first HOT lanes in the country, the MnPASS lanes are the first implementation of road pricing in Minnesota and possess a dynamic pricing schedule. Tolls charged to single occupancy vehicles (SOVs) are adjusted every three minutes according to HOT lane vehicle density. Given the infancy of systems like MnPASS, questions remain about drivers responses to toll prices. Three field experiments were conducted on the corridors during which prices were changed. Data from the field experiments as well as two years of toll and traffic data were analyzed to measure driver responses to pricing changes. Driver elasticity to price was positive with magnitudes less than 1.0. This positive relationship between price and demand is in contrast with the previously held belief that raising the price would discourage demand. In addition, drivers consistently paid between approximately $60-120 per hour of travel time savings, much higher than MnDOT’s value of time (VOT) of $15/hr. Reasons for this include the value drivers place on reliability, a misperception about the actual time savings and that MnPASS users have a greater VOT than the average driver. Four alternative pricing strategies are then proposed. These pricing strategies were tested using a HOT lane choice model based on previous research. The share of transponder owning SOVs using the MnPASS lane was measured against price producing positive elasticity values at lower prices and negative elasticity values at higher prices. MnPASS lane usage rises with price at lower tolls due to the increased time savings benefit but is eventually outweighed by the price, causing the lane share to decrease at higher tolls.

The advancements of GPS and GIS technologies provide new opportunities for investigating vehicle trip generation and destination choice at the microscopic level. This research models how land use and road network structure influence non-work, non-home vehicle trip generation and non-work destination choice in the context of trip chains, using the in-vehicle GPS travel data in the Minneapolis-St. Paul Metropolitan Area. This research includes three key parts: modeling non-work vehicle trip generation, modeling non-work, single-destination choice, and modeling non-work, two-destination choice. This research contributes to methodologies in modeling single-destination choice and multiple-destination choice and tests several hypotheses which were not investigated before.

In modeling non-work vehicle trip generation, this research identifies correlation of trips made by the same individual in the trip generation models. To control for this effect, five mixed-effects models are systematically applied: mixed-effects linear model, mixed-effects log-linear model, mixed-effects negative binomial model, and mixed-effects ordered logistic model. The mixed-effects ordered logistic model produces the highest goodness of fit for our data and therefore is recommended.

In modeling non-work, single-destination choice, this research proposes a new method to build choice sets which combines survival analysis and random sampling. A systematic comparison of the goodness of fit of models with various choice set sizes is also performed to determine an appropriate choice set size. In modeling non-work, multiple-destination choice, this research proposes and compare three new approaches to build choice sets for two-destination choice in the context of trip chains. The outcomes of these approaches are empirically compared and we recommend the major/minor-destination approach for modeling two-destination choice. The modeling procedure can be expanded to trip chains with more than two destinations.

Our empirical findings reveal that:

Although accessibility around home is not found to have statistically significant effects on non-work vehicle trips, the diversity of services within 10 to 15 minutes and 15 and 20 minutes from home can help reduce the number of non-work vehicle trips.

Accessibility and diversity of services at destinations influence destination choice but they do not exert the same level of impact. The major destination in a trip chain tends to influence the decision more than the minor destination.

The more dissimilar the two destinations in a trip chain are, the more attractive the trip chain is.

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Congratulations to soon to be Dr. Carlos Carrion (shown in the center of the picture, between alums Nebiyou Tilahun and Pavithra Parthasarthi), who recently defended his Ph.D. Thesis “Travel Time Perception Errors: Causes and Consequences” (a draft of which is linked). He is working as a post-doctoral researcher at MIT/SMART in Singapore.

Travel Time Perception Errors: Causes and Consequences
Abstract:
This research investigates the causes, and consequences behind travel time perception. Travel times are experienced. Thus, travelers estimate the travel time through their own perception. This is the underlying reason behind the mismatch between travel times as reported by a traveler (subjective travel time distribution) and travel times as measured from a device (e.g. loop detector or GPS navigation device; objective travel time distribution) in collected data. It is reasonable that the relationship between subjective travel times and objective travel times may be expressed mathematically as: Ts = To + ξ. Ts is a random variable associated with the probability density given by the subjective travel time distribution. To is a random variable associated with the probability density given by the objective travel time distribution. The variable ξ is the random perception error also associated with its own probability density. Thus, it is clear that travelers may overestimate or underestimate the measured travel times, and this is likely to influence their decisions unless E(ξ) = 0, and Var(ξ) ≈ 0. In other words, travelers are “optimizing” (i.e. executing decisions) according to their own divergent views of the objective travel time distribution.
This dissertation contributes novel results to the following areas of transportation research: travel time perception; valuation of travel time; and route choice modeling. This study presents a systematic identification of factors that lead to perception errors of travel time. In addition, the factors are related to similar factors on time perception research in psychology. These factors are included in econometric models to study their influence on travel time perception, and also identify which of these factors lead to overestimation or underestimation of travel times. These econometric models are estimated on data collected from commuters recruited from a previous research study in the Minneapolis-St. Paul region (Carrion and Levinson, 2012a, Zhu, 2010). The data (surveys, and Global Positioning System [GPS] points) consists of work trips (from home to work, and from work to home) of subjects. For these work trips, the subjects’ self-reported travel times, and the subjects’ travel times measured by GPS devices were collected. Furthermore, this dissertation provides the first empirical results that highlight the influence of perception errors in the valuation of travel time, and in the dynamic behavior of travelers’ route choices. Last but not least important, this dissertation presents the most comprehensive literature review of the value of travel time reliability written to date.

The CEIE Department is pleased to welcome Dr. Shanjiang Zhu to the CEIE Department in August 2012. Dr. Zhu obtained his doctorate from the Department of Civil Engineering, University of Minnesota, in 2010, and extended his research in transportation planning and engineering as a research scientist at University of Maryland for two years. His research interests include agent-based travel demand models, integrated models of micro-simulation and macroscopic demand models, applications of GIS/GPS in transportation modeling, sustainable transportation, and transportation economics. He enhances the current curricula by integrating problem-oriented teaching philosophy with his research experience in various transportation-related projects, especially those with immediate applications in addressing local transportation problems. Dr. Zhu is currently teaching the Introduction to Transportation Engineering (CEIE 360). He looks forward to working with CEIE students both in the classroom and on transportation-related research projects.”

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Nexus alumnus, and new father, Nebiyou Tilahun was recently appointed to an assistant professor position at the University of Illinois at Chicago:

Dr. Tilahun is currently an Assistant Professor in the Department of Urban Planning and Policy at the University of Illinois at Chicago. He received his Ph.D. from the University of Minnesota in 2010. His research interests are in transportation planning, travel behavior, the study of travel for social activities, and the use of agent based models for transportation planning applications. His dissertation, Matching Home and Work: Job Search, Contacts and Travel, developed a framework for work trip distribution from the perspective of the job search process. Between May 2009 and December 2011, he successively held postdoctoral researcher positions at the Urban Transportation Center (UIC) and the Hubert Humphrey School of Public Affairs (UMN) working on issues related to Job Access and Reverse Commute and Human Services Transportation (at UIC), and linking transit accessibility to the regional economy in the Twin Cities (at UMN). As a graduate student he was a member of the NeXuS research group. Previously he also worked as a Transportation Engineer at the Washington State Department of Transportation (2001-2002). Dr. Tilahun’s Civil Engineering studies started in Ethiopia at Addis Ababa University’s Faculty of Technology. During the Fall of 2012 he will be teaching UPP 502 Planning Skills: Computers, Methods and Communications and UPP 562: Urban Transportation III: Laboratory.

Abstract: The objective of this Faculty Early Career Development (CAREER) Program award is to investigate how individual travel behavior (e.g., route choice, trip scheduling, and selection of transportation mode) and transportation-related organizational decision-making (e.g., investment and pricing decisions) impact travel reliability (percentage of on-time arrival at destination). This research tests the hypothesis that minor behavior changes at the individual or organizational level leads to significant changes in travel reliability. The theory explains how individuals and organizations actually make transportation-related decisions, recognizing that they do not have perfect information or unlimited computational capabilities. The empirical portion of the research addresses a gap in the transportation science literature by employing smart phones as mobile GPS sensors to collect travel behavior data.
If successful, this project will provide decision-support tools that could help transform transportation systems operations and planning practices. These tools will enable transportation agencies to assess strategies that induce individual and organizational behavioral changes (e.g., increased transit ridership, improved trip departure time choice, better route diversion decisions, and more cost-effective transportation investments) that could mitigate traffic congestion and improve travel reliability. Over the long run, a more efficient and reliable transportation system will stimulate economic growth, enhance quality of life, and support emergency response. As this research breaks traditional disciplinary boundaries between the behavioral sciences and systems engineering, it also sets the stage for a new research direction that focuses on optimizing transportation system performance based on how choices are actually made, not how they should be made. This project will involve high school undergraduate, and graduate underrepresented students in various research tasks. Research findings will be broadly distributed through a K-12 Transportation Education Web Portal, an open-access Wiki site, and other professional and community outreach efforts.

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Lee Munnich passes on news that famed transportation economist, Herb Mohring, passed away on June 4. His biography in wikipedia is below:

Herbert Mohring: “Herbert Mohring was a transportation economist who taught at the University of Minnesota from 1961-1994. He received his Ph.D. from Massachusetts Institute of Technology in 1959.
He is widely known for his identification of what was dubbed the Mohring effect of increasing returns in public transportation (see: Mohring (1972) for details).
Mohring and Harwitz (1962) also showed that the revenues from the first-best congestion tax exactly cover the construction costs of highways when highways possess constant returns to scale.
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